Electroencephalography (EEG) signals are frequently used for various Brain-Computer Interface (BCI) tasks. While Deep Learning (DL) techniques have shown promising results, they are hindered by the substantial data requirements. By leveraging data from multiple subjects, transfer learning enables more effective training of DL models. A technique that is gaining popularity is Euclidean Alignment (EA) due to its ease of use, low computational complexity, and compatibility with Deep Learning models. However, few studies evaluate its impact on the training performance of shared and individual DL models. In this work, we systematically evaluate the effect of EA combined with DL for decoding BCI signals. We used EA to train shared models with data from multiple subjects and evaluated its transferability to new subjects. Our experimental results show that it improves decoding in the target subject by 4.33% and decreases convergence time by more than 70%. We also trained individual models for each subject to use as a majority-voting ensemble classifier. In this scenario, using EA improved the 3-model ensemble accuracy by 3.7%. However, when compared to the shared model with EA, the ensemble accuracy was 3.62% lower.

翻译：脑电图（Electroencephalography，EEG）信号广泛应用于各类脑机接口（Brain-Computer Interface，BCI）任务。尽管深度学习（Deep Learning，DL）技术已展现出令人瞩目的成果，但其应用受限于大量的数据需求。通过利用多受试者数据，迁移学习能够更有效地训练DL模型。欧几里得对齐（Euclidean Alignment，EA）因其易于使用、计算复杂度低以及与深度学习模型的兼容性而日益流行。然而，少有研究评估其对共享模型和个体模型训练性能的影响。本文系统评估了EA结合DL在解码BCI信号中的效果。我们利用EA基于多受试者数据训练共享模型，并评估其向新受试者的迁移性。实验结果表明，EA将目标受试者的解码准确率提升了4.33%，并将收敛时间缩短超过70%。同时，我们对每位受试者训练个体模型，并将其用于多数投票集成分类器。在此场景下，使用EA使3模型集成准确率提升了3.7%。然而，与结合EA的共享模型相比，集成准确率仍低3.62%。